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Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n= 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n= 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n= 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.

743 Background: The prognosis of metastatic pancreatic ductal adenocarcinoma (mPDAC) remains poor with a median survival time of 10-12 months. First-line treatment is largely influenced by performance status with fit patients more often receiving FOLFIRINOX (FFX) than Gemcitabine+Nab-Paclitaxel (GNP). Although the two regimens have improved outcomes over gemcitabine monotherapy, no biomarkers routinely used in clinical practice can predict which regimen is optimal to facilitate a precision medicine approach. We developed two signatures (V-FFX and V-GNP) associated with treatment outcomes for the respective first-line regimens using a retrospective cohort of mPDAC cases. Methods: We conducted a retrospective study of mPDAC patients treated at two institutions (UPMC and Cedars Sinai) from 2014 to 2021. Digitized histological H&E sections corresponding to 145 metastatic PDAC patients treated with either first-line FFX or GNP were included. Independent randomized training and test datasets were constructed for FFX-treated (train: 41, test: 25) and GNP-treated (train: 49, test: 30) patients. To construct the histological assay, a deep-learning algorithm then segmented nuclei to extract quantitative histological features. Features associated with disease-specific survival (DSS) for FFX and GNP were identified utilizing univariate Cox proportional hazards (CPH) models for the respective training sets and V-FFX and V-GNP signatures were constructed. DSS stratification of the V-FFX and V-GNP signatures were examined using Kaplan-Meier analysis and the log-rank test and DSS percentages at 12 months were calculated on the respective test sets. Results: The V-FFX and V-GNP signatures were found to be significantly associated with treatment outcomes stratified in the respective test sets (log-rank test, V-FFX: p=0.046, V-GNP: p=0.004). 29 of 55 patients tested positive for only one of either V-FFX and V-GNP signatures. Kaplan-Meier analysis demonstrated robust separation with hazard ratios for the V-FFX and V-GNP signatures of 3.01 (95% CI: 0.96, 9.45) and 4.81 (95% CI: 1.74, 13.3). DSS at 12 months for patients in V-FFX +ve vs -ve groups were 88% (8/9) vs 50% (7/14). DSS at 12 months for patients in V-GNP +ve vs -ve groups were 66% (8/12) vs 15% (2/13). Conclusions: AI derived V-FFX and V-GNP morphological signatures were strongly associated with treatment outcomes for first-line FFX and GNP and can potentially aid in the selection of first-line treatment for mPDAC patients.

Background: The prognosis for patients diagnosed with pancreatic ductal adenocarcinoma remains poor, even after successful resection. While multiple regimens have proven to improve outcomes following resection, no biomarkers routinely used in clinical practice can predict which regimen is optimal for an individual patient to facilitate a precision medicine approach. Artificial intelligence (AI) approaches can enable the identification of subvisual morphologic features in digital scans of routine histologic slides that are associated with specific treatment responses. Previous work had developed an AI-derived morphologic signature correlated with response to gemcitabine-based chemotherapy in resected PDAC specimens from The Cancer Genome Atlas (TCGA) (c-index:0.69). We validate this previously developed signature on an external cohort of postoperatively treated PDAC cases from a single institution. Methods: Digitized histological H&E TMA sections corresponding to 45 post-operatively treated resected PDAC patients from 2011-2015 were used in this study. Of the 45 patients, 22 were neoadjuvantly treated with either gemcitabine or 5-FU backbone cytotoxic chemotherapy. Using the histologic images, we extracted nuclei images from tissue regions using segmentation models and computed geometric features of these nuclei. Patients were stratified by the signature previously associated with gemcitabine response in a dataset from TCGA into low and high risk groups, and Disease Specific Survival (DSS) and Recurrence Free Survival (RFS) was compared between the stratified groups via Kaplan Meier estimators and log-rank test. Results: The morphologic signature previously found to be associated with gemcitabine treatment response stratified both DSS and RFS in the external cohort (log-rank test, DSS: p=0.03, RFS: p=0.01). A set of features describing variations in nuclear geometry were most correlated with the prediction, with increased variance being associated with higher risk. Kaplan-Meier analysis demonstrated the signature was able to separate the cohort robustly with a statistically significant hazard ratio of 0.45 [95% CI 0.22, 0.93] for DSS and 0.39 [95% CI 0.19, 0.77] for RFS. The median DSS was 16 months (95% CI: 10.9, 50.1) in the high risk group and 43 months (95% CI: 26.8, 63.8) in the low risk group, a difference of 27 months. Similarly, the median RFS was 9.1 months (95% CI: 6.1, 14.7) in the high risk group and 22.6 months (95% CI: 14.1, 44.8) in the low risk group, a difference of 13.5 months. Conclusion: The AI derived Valar morphological signature previously found to be associated with gemcitabine treatment response effectively stratifies patients into low and high risk groups in an external resected PDAC cohort (hazard ratio: 0.45 for DSS, 0.39 for RFS). Citation Format: Vrishab Krishna, Viswesh Krishna, Ekin Tiu, Vivek Nimgaonkar, Damir Vrabac, Katelyn Smith, Anirudh Joshi, Aatur Singhi, Eric Collisson. Validation of an artificial intelligence derived histological biomarker for gemcitabine response in resected pancreatic ductal adenocarcinoma (PDAC) [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A043.

In this letter we present a deterministic discrete-time networked SEIR model that includes a number of transportation networks, and present assumptions under which it is well defined. We analyze the limiting behavior of the model and present necessary and sufficient conditions for estimating the spreading parameters from data. We illustrate these results via simulation and with real COVID-19 data from the Northeast United States, integrating transportation data into the results.

e16295 Background: Adjuvant chemotherapy improves survival following resection of pancreatic ductal adenocarcinoma (PDAC). A modified fluorouracil/irinotecan/oxaliplatin regimen (mFOLFIRINOX) has demonstrated improved disease free survival and overall survival, though gemcitabine-based monotherapy and gemcitabine plus capecitabine are alternatives in less fit patients. Though there are several proposed biomarkers to guide treatment decisions (GATA6, hENT1, and GemPred), no biomarker is used to guide treatment selection in clinical practice. Consequently, we sought to develop an artificial intelligence-derived signature of features from digital images of routine histopathology specimens that could identify patients susceptible to routine chemotherapeutic agents. Methods: 139 whole-slide digitized histological slides corresponding to 102 resected PDAC tumors from TCGA-PAAD were used in this study. This dataset corresponded to patients that had received either gemcitabine-backbone or 5 FU-backbone chemotherapy as their first-line adjuvant treatment. We extracted nuclei images from tissue regions using segmentation models and computed geometric features of these nuclei which we then correlated with Disease Specific Survival (DSS) in order to construct a signature associated with treatment benefit. This signature was compared against two board certified pathologists using the grade of the digital slides images to classify patients into above or below average DSS buckets. Results: Among quantitative geometric features, a set of area and ellipse features describing nuclei geometry correlated most with response to gemcitabine (R̃0.4). The cox proportional hazards model using these geometric nuclei features was found to be predictive of response to gemcitabine and achieved a C-index (95% CI) of 0.69 (0.58, 0.79). The pathologist-based baseline model for above and below average DSS had a median DSS of 443 and 461 days respectively. Using the average expected lifetime as the threshold, the model divides patients receiving gemcitabine into two histological subtypes with median DSS of 586 and 394 days respectively (p < 0.05). The model appeared specific to gemcitabine. Among patients receiving 5-FU (n = 10) there was no statistical significance in median DSS between the subtypes and a c-index of 0.63 (0.27, 1.0). Conclusions: An artificial intelligence approach utilizing only routine histopathology can identify features that correlate with treatment outcomes in PDAC with classification performance (c-index:0.69) superior to the validated AJCC treatment prediction tool (0.59).

The application of deep learning to pathology assumes the existence of digital whole slide images of pathology slides. However, slide digitization is bottlenecked by the high cost of precise motor stages in slide scanners that are needed for position information used for slide stitching. We propose GloFlow, a two-stage method for creating a whole slide image using optical flow-based image registration with global alignment using a computationally tractable graph-pruning approach. In the first stage, we train an optical flow predictor to predict pairwise translations between successive video frames to approximate a stitch. In the second stage, this approximate stitch is used to create a neighborhood graph to produce a corrected stitch. On datasets of simulated video scans of pathology slides, we find that our method outperforms known approaches to slide-stitching, and stitches images resembling those produced by slide scanners. Our method allows for creation of whole slide images using widely-available low cost microscopes.

In this paper we investigate estimating the parameters of a discrete time networked virus spread model from time series data. We explore the effect of multiple challenges on the estimation process including system noise, missing data, time-varying network structure, and quantization of the measurements. We also demonstrate how well a heterogeneous model can be captured by homogeneous model parameters. We further illustrate these challenges by employing recent data collected from the ongoing 2019 novel coronavirus (2019-nCoV) outbreak, motivating future work.

In this paper we introduce a discrete time competing virus model and the assumptions necessary for the model to be well posed. We analyze the system exploring its different equilibria. We provide necessary and sufficient conditions for the estimation of the model parameters from time series data and introduce an online estimation algorithm. We employ a dataset of two competing subsidy programs from the US Department of Agriculture to validate the model by employing the identification techniques. To the best of our knowledge, this work is the first to study competing virus models in discrete-time, online identification of spread parameters from time series data, and validation of said models using real data. These new contributions are important for applications since real data is naturally sampled., arXiv admin note: text overlap with arXiv:1710.11149

Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic analysis of histology sections from 209 DLBCL cases with associated clinical and cytogenetic data. Duplicate tissue core sections were arranged in tissue microarrays (TMAs), and replicate sections were stained with H&E and immunohistochemical stains for CD10, BCL6, MUM1, BCL2, and MYC. The TMAs are accompanied by pathologist-annotated regions-of-interest (ROIs) that identify areas of tissue representative of DLBCL. We used a deep learning model to segment all tumor nuclei in the ROIs, and computed several geometric features for each segmented nucleus. We fit a Cox proportional hazards model to demonstrate the utility of these geometric features in predicting survival outcome, and found that it achieved a C-index (95% CI) of 0.635 (0.574,0.691). Our finding suggests that geometric features computed from tumor nuclei are of prognostic importance, and should be validated in prospective studies., Corrections to folder structure figure